

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       List[Any]						):
		__UpperCAmelCase      		= tempfile.mkdtemp()

		# fmt: off
		__UpperCAmelCase      		= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
		# fmt: on
		__UpperCAmelCase      		= os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES['''vocab_file''']						)
		with open(self.vocab_file			,    '''w'''			,    encoding='''utf-8'''						) as vocab_writer:
			vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]						)						)

		__UpperCAmelCase      		= {
		    '''do_resize''': True,
		    '''size''': {'''height''': 18, '''width''': 18},
		    '''do_normalize''': True,
		    '''image_mean''': [0.5, 0.5, 0.5],
		    '''image_std''': [0.5, 0.5, 0.5],
		}
		__UpperCAmelCase      		= os.path.join(self.tmpdirname			,    _lowercase						)
		with open(self.image_processor_file			,    '''w'''			,    encoding='''utf-8'''						) as fp:
			json.dump(_lowercase			,    _lowercase						)



	def        a  (  self     :       Optional[int]			,    **_lowercase     :       List[str]						):
		return BertTokenizer.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Optional[int]			,    **_lowercase     :       List[str]						):
		return ViTImageProcessor.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Dict						):
		shutil.rmtree(self.tmpdirname						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= [np.random.randint(2_55			,    size=(3, 30, 4_00)			,    dtype=np.uinta						)]

		__UpperCAmelCase      		= [Image.fromarray(np.moveaxis(_lowercase			,    0			,    -1						)						) for x in image_inputs]

		return image_inputs



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_image_processor()

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(tokenizer=_lowercase			,    image_processor=_lowercase						)

		processor.save_pretrained(self.tmpdirname						)
		__UpperCAmelCase      		= VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

		self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer.get_vocab()						)
		self.assertIsInstance(processor.tokenizer			,    (BertTokenizer, BertTokenizerFast)						)

		self.assertEqual(processor.image_processor.to_json_string()			,    image_processor.to_json_string()						)
		self.assertIsInstance(processor.image_processor			,    _lowercase						)



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(
		    tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor()						)
		processor.save_pretrained(self.tmpdirname						)

		__UpperCAmelCase      		= self.get_tokenizer(bos_token='''(BOS)'''			,    eos_token='''(EOS)'''						)
		__UpperCAmelCase      		= self.get_image_processor(do_normalize=_lowercase			,    padding_value=1.0						)

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor.from_pretrained(
		    self.tmpdirname			,    bos_token='''(BOS)'''			,    eos_token='''(EOS)'''			,    do_normalize=_lowercase			,    padding_value=1.0						)

		self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer_add_kwargs.get_vocab()						)
		self.assertIsInstance(processor.tokenizer			,    (BertTokenizer, BertTokenizerFast)						)

		self.assertEqual(processor.image_processor.to_json_string()			,    image_processor_add_kwargs.to_json_string()						)
		self.assertIsInstance(processor.image_processor			,    _lowercase						)



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= self.get_image_processor()
		__UpperCAmelCase      		= self.get_tokenizer()

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(tokenizer=_lowercase			,    image_processor=_lowercase						)

		__UpperCAmelCase      		= self.prepare_image_inputs()

		__UpperCAmelCase      		= image_processor(_lowercase			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= processor(images=_lowercase			,    return_tensors='''np'''						)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,    input_processor[key].sum()			,    delta=1E-2						)



	def        a  (  self     :       List[str]						):
		__UpperCAmelCase      		= self.get_image_processor()
		__UpperCAmelCase      		= self.get_tokenizer()

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(tokenizer=_lowercase			,    image_processor=_lowercase						)

		__UpperCAmelCase      		= '''lower newer'''

		__UpperCAmelCase      		= processor(text=_lowercase						)

		__UpperCAmelCase      		= tokenizer(_lowercase						)

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]						)



	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= self.get_image_processor()
		__UpperCAmelCase      		= self.get_tokenizer()

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(tokenizer=_lowercase			,    image_processor=_lowercase						)

		__UpperCAmelCase      		= '''lower newer'''
		__UpperCAmelCase      		= self.prepare_image_inputs()

		__UpperCAmelCase      		= processor(text=_lowercase			,    images=_lowercase						)

		self.assertListEqual(list(inputs.keys()						)			,    ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']						)

		# test if it raises when no input is passed
		with self.assertRaises(_lowercase						):
			processor()



	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= self.get_image_processor()
		__UpperCAmelCase      		= self.get_tokenizer()

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(tokenizer=_lowercase			,    image_processor=_lowercase						)

		__UpperCAmelCase      		= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		__UpperCAmelCase      		= processor.batch_decode(_lowercase						)
		__UpperCAmelCase      		= tokenizer.batch_decode(_lowercase						)

		self.assertListEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.get_image_processor()
		__UpperCAmelCase      		= self.get_tokenizer()

		__UpperCAmelCase      		= VisionTextDualEncoderProcessor(tokenizer=_lowercase			,    image_processor=_lowercase						)

		__UpperCAmelCase      		= '''lower newer'''
		__UpperCAmelCase      		= self.prepare_image_inputs()

		__UpperCAmelCase      		= processor(text=_lowercase			,    images=_lowercase						)

		self.assertListEqual(list(inputs.keys()						)			,    processor.model_input_names						)







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



